Ant Colony Optimization for Tourist Route
Ant Colony Optimization is a relatively new meta-heuristic that has proven its quality and versatility on various combinatorial optimization problems such as the traveling salesman problem, the vehicle routing problem and the job shop scheduling problem. The inspiring source of ACO is the pheromone...
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2005
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| 在線閱讀: | https://etd.uum.edu.my/1295/1/NOPPARAT_MEEPLAT.pdf https://etd.uum.edu.my/1295/2/1.NOPPARAT_MEEPLAT.pdf |
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my-uum-etd.12952013-07-24T12:11:19Z Ant Colony Optimization for Tourist Route 2005-08-23 Meeplat, Nopparat Faculty of Information Technology Faculty of Information Technology QA299.6-433 Analysis Ant Colony Optimization is a relatively new meta-heuristic that has proven its quality and versatility on various combinatorial optimization problems such as the traveling salesman problem, the vehicle routing problem and the job shop scheduling problem. The inspiring source of ACO is the pheromone trail laying and following behavior of real ants, which use pheromones as a communication medium. In this project the ACO algorithm to routing problems in traveling cities under static and dynamic conditions. This study is divided into three parts. The first part aims to identify various connecting cities in Thailand with appropriate distances. The second part of this research involves formulating and applying the ACO algorithms to find the shortest path based on the distance calculated from source to destination cities. The ACO routing will then be applied on the constructed cities, taking into consideration different traffic conditions. The final part of the study focused on finding the shortest path and calculation of cost based on the distance traveled. 2005-08 Thesis https://etd.uum.edu.my/1295/ https://etd.uum.edu.my/1295/1/NOPPARAT_MEEPLAT.pdf application/pdf eng validuser https://etd.uum.edu.my/1295/2/1.NOPPARAT_MEEPLAT.pdf application/pdf eng public masters masters Universiti Utara Malaysia |
| institution |
Universiti Utara Malaysia |
| collection |
UUM ETD |
| language |
eng eng |
| topic |
QA299.6-433 Analysis |
| spellingShingle |
QA299.6-433 Analysis Meeplat, Nopparat Ant Colony Optimization for Tourist Route |
| description |
Ant Colony Optimization is a relatively new meta-heuristic that has proven its quality and versatility on various combinatorial optimization problems such as the traveling
salesman problem, the vehicle routing problem and the job shop scheduling problem. The inspiring source of ACO is the pheromone trail laying and following behavior of real ants, which use pheromones as a communication medium. In this project the ACO algorithm to routing problems in traveling cities under static and dynamic conditions. This study is divided into three parts. The first part aims to identify various connecting cities in Thailand with appropriate distances. The second part of this research involves formulating and applying the ACO algorithms to find the shortest path based on the distance calculated from source to destination cities. The ACO routing will then be applied on the constructed cities, taking into consideration different traffic conditions. The final part of the study focused on finding the shortest path and calculation of cost based on the distance traveled.
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| format |
Thesis |
| qualification_name |
masters |
| qualification_level |
Master's degree |
| author |
Meeplat, Nopparat |
| author_facet |
Meeplat, Nopparat |
| author_sort |
Meeplat, Nopparat |
| title |
Ant Colony Optimization for Tourist Route |
| title_short |
Ant Colony Optimization for Tourist Route |
| title_full |
Ant Colony Optimization for Tourist Route |
| title_fullStr |
Ant Colony Optimization for Tourist Route |
| title_full_unstemmed |
Ant Colony Optimization for Tourist Route |
| title_sort |
ant colony optimization for tourist route |
| granting_institution |
Universiti Utara Malaysia |
| granting_department |
Faculty of Information Technology |
| publishDate |
2005 |
| url |
https://etd.uum.edu.my/1295/1/NOPPARAT_MEEPLAT.pdf https://etd.uum.edu.my/1295/2/1.NOPPARAT_MEEPLAT.pdf |
| _version_ |
1747827115008458752 |